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<p>This study introduces the threshold convergence algorithm (TCA), a dynamic two-stage framework integrating Taguchi orthogonal arrays and weighted grey relational analysis (wGRA) to optimize hybrid fiber-reinforced concrete (HFRC). The hybrid effects of steel and polypropylene fibers on mechanical and fresh properties were evaluated, with compressive strength, split tensile strength, impact energy, and workability as key metrics. A unique iterative threshold adjustment mechanism in TCA addressed material variability, reducing experimental trials by about 91% compared to full factorial designs. Unlike conventional optimization approaches, which typically employ a fixed-design Taguchi method with static threshold criteria, the TCA dynamically adjusts threshold levels, allowing it to more effectively accommodate the heterogeneity present in fiber-reinforced concrete systems. The optimal mix (0.2% polypropylene, 0.25% steel fibers) achieved a 12.75% increase in compressive strength, 26.08% in split tensile strength, a 13.97% increase in impact ductility index compared to conventional optimization method. This work bridges gaps in multi-criteria optimization for HFRC, offering a scalable solution for resilient infrastructure.</p>

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A Dynamic Threshold Convergence Algorithm for Hybrid Fiber-Concrete Optimization Using Modified Taguchi and Grey Relational Methods

  • 期刊论文

  • 发表日期：

    2025年5月30日

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  • Muhammad Aftab Khan,
  • Qaiser Uz Zaman Khan,
  • Samaha Badi Uz Zaman Khan,
  • Syed Saqib Mehboob,
  • Aisha Tayyab

摘要

This study introduces the threshold convergence algorithm (TCA), a dynamic two-stage framework integrating Taguchi orthogonal arrays and weighted grey relational analysis (wGRA) to optimize hybrid fiber-reinforced concrete (HFRC). The hybrid effects of steel and polypropylene fibers on mechanical and fresh properties were evaluated, with compressive strength, split tensile strength, impact energy, and workability as key metrics. A unique iterative threshold adjustment mechanism in TCA addressed material variability, reducing experimental trials by about 91% compared to full factorial designs. Unlike conventional optimization approaches, which typically employ a fixed-design Taguchi method with static threshold criteria, the TCA dynamically adjusts threshold levels, allowing it to more effectively accommodate the heterogeneity present in fiber-reinforced concrete systems. The optimal mix (0.2% polypropylene, 0.25% steel fibers) achieved a 12.75% increase in compressive strength, 26.08% in split tensile strength, a 13.97% increase in impact ductility index compared to conventional optimization method. This work bridges gaps in multi-criteria optimization for HFRC, offering a scalable solution for resilient infrastructure.